Glocal Alignment for Unsupervised Domain Adaptation

Traditional unsupervised domain adaptation methods attempt to align source and target domains globally and are agnostic to the categories of the data points. This results in an inaccurate categorical alignment and diminishes the classification performance on the target domain. In this paper, we alter existing adversarial domain alignment methods to adhere to category alignment by imputing category information. We partition the samples based on category using source labels and target pseudo labels and then apply domain alignment for every category. Our proposed modification provides a boost in performance even with a modest pseudo label estimator. We evaluate our approach on 4 popular domain alignment loss functions using object recognition and digit datasets.